Sleep-related fall monitoring among elderly using non-invasive wireless bio-sensors

dc.contributor.advisorLight, Janet
dc.contributor.authorLi, Xiaoyi
dc.description.abstractHuman cognitive function decreases with aging thereby increasing the risk of fall. A fall can cause severe injury, long hospitalization time, and often affects an individual's quality of life. Fall data obtained from a nursing home in New Brunswick shows that 50% of fall incidents happen during night sleep. In this thesis, a fall detection and prediction system is developed in which sleep brain activity is captured and analyzed in real time. A fall classification method for the brain signals captured as Electroencephalography is developed using Support Vector Machine (SVM) and Time-Frequency Kernels. In this fall-prediction system, a patient wears a hat with a light weight wireless biosensor device to capture EEG signals, and then sent wirelessly to a back-end server for real-time analysis of the data sets. Over the supervised training period, the server gets enough data from a subject and starts to learn the threshold value between normal and abnormal EEG for the subject. When the system is trained with signature data, it gives more accurate detection result.
dc.description.copyrightNot available for use outside of the University of New Brunswick
dc.description.note(UNB accession number) Thesis 9436. (OCoLC) 904053892.
dc.format.extentix, 67 pages ; illustrations (some colour)
dc.identifier.otherThesis 9436
dc.publisherUniversity of New Brunswick
dc.subject.disciplineComputer Science
dc.subject.lcshElectroencephalography--Age factors--Data processing.
dc.subject.lcshFalls (Accidents) in old age.
dc.subject.lcshSupport vector machines.
dc.subject.lcshWireless sensor networks.
dc.subject.lcshSleep--Age factors.
dc.subject.lcshSleep--Physiological aspects--Observations.
dc.titleSleep-related fall monitoring among elderly using non-invasive wireless bio-sensors
dc.typemaster thesis Science of Computer Science of New Brunswick


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